Azret Botash

Objectives: Extracochlear electrodes (EEs), defined as electrode contacts located outside the cochlea, can degrade cochlear implant (CI) performance. We present and validate an Electric Field Imaging (EFI)-based algorithm that helps detect EEs and can be used intra- or post-operatively. Methods: An algorithm for detecting EEs from EFI data was developed. Validation and testing were performed using three datasets: (a) bench models with known EE conditions (saline and resistive load model), (b) clinical EFI recordings from CI recipients with imaging confirmation (CT or plain X-ray), and (c) EFI recordings without imaging. Primary outcomes included the ability to differentiate extracochlear conditions from fully inserted electrode arrays, as well as the concordance between the number of EE contacts identified by the algorithm and those determined from controlled bench configurations or available imaging data. Results: The algorithm reliably differentiated full insertion from EE conditions on bench models and clinical EFI recordings from CI recipients with imaging confirmation. In the imaging-confirmed clinical cohort (6 EE cases among 226 CI recipients), the algorithm achieved 100% sensitivity and specificity. In clinical cases without imaging, the algorithm flagged 3.94% as having extracochlear electrodes, within the range of prevalence reported in the literature. Furthermore, the lateral-wall electrode arrays were associated with a significantly higher extracochlear electrode occurrence compared to the pre-curved arrays. Conclusions: This EFI-based algorithm may provide a useful screening tool for EEs using routine clinical measurements, supporting intra-operative and post-operative detection while reducing reliance on imaging and thereby aiding clinical decision-making. The method can be integrated into existing clinical fitting software workflow and complements recent work on EFI-based tip fold-over detection. The observed sensitivity and specificity suggest that this approach may provide a valuable tool for identifying EEs in situations where imaging is unavailable or impractical.

Objectives: Tip fold-over (TFO) is a rare but critical occurrence in cochlear implant procedures where the electrode array folds back on itself within the cochlea, compromising programming and device performance. Timely intraoperative detection is essential for immediate correction and optimal placement. Electric field imaging (EFI) has shown promise for identifying TFO both intra- and post-operatively. This study evaluates the performance of a TFO detection algorithm implemented in Target CI (Version 1.6) using Advanced Bionics’ cochlear implant systems, validated through bench and patient datasets. Methods: Sample data included (1) bench testing with a plastic cochlea and human temporal bones with and without induced TFOs, confirmed visually or radiographically; (2) intraoperative EFI measurements recorded using the AIM™ system, with electrode placement confirmed through imaging; and (3) historical EFI recordings from the Target CI DataLake, which lacks imaging and programming metadata. The TFO algorithm’s performance was evaluated by assessing its sensitivity and specificity using these datasets. Results: The TFO algorithm achieved 100% sensitivity and specificity in bench models and intraoperative EFI with imaging-confirmed placements. Among 226 intra-op cases, four TFOs were confirmed by imaging, and all were correctly identified by the algorithm. In the large set of DataLake cases (14,734 implants), 0.80% were flagged as potential TFOs. TFO prevalence was higher with pre-curved arrays (1.22%) than straight lateral wall arrays (0.32%). Conclusions: The TFO algorithm showed high reliability with 100% sensitivity and specificity using routine clinical EFI data. While not a replacement for imaging, the TFO algorithm serves as a fast, accessible tool to alert clinicians to potential TFOs.